使用MNIST进行Logistic回归实现-无法收敛?

问题描述

我希望有人能帮助我.我从头开始实现了逻辑回归(因此没有库，除了Python中的numpy).

I hope someone can help me. I did an implementation of logistic regression from scratch (so without library, except numpy in Python).

我使用 MNIST数据集作为输入，并决定尝试(因为我正在做二进制分类)仅对两位数字进行测试:1和2.我的代码可在此处找到

I used MNIST dataset as input, and decided to try (since I am doing binary classification) a test on only two digits: 1 and 2. My code can be found here

https ://github.com/michelucci/Logistic-Regression-Explained/blob/master/MNIST%20with%20Logistic%20Regression%20from%20scratch.ipynb

笔记本应在装有必需库的任何系统上运行.

The notebook should run on any system that have the necessary library installed.

不知何故，我的成本函数没有收敛.由于我的A(我的S形)等于1，因为z很大，所以我得到了错误.

Somehow my cost function is not converging. I am getting error since my A (my sigmoid) is getting equal to 1, since z is getting very big.

我尝试了所有操作，但没有看到我的错误.谁能看一下，让我知道我是否错过了明显的事情?这里的重点不是高精度.正在使模型收敛到某种东西；)

I tried everything but I don't see my error. Can anyone give a look and let me know if I missed something obvious? The point here is not getting a high accuracy. Is getting the model to converge to something ;)

预先感谢，翁贝托

推荐答案

我收到了错误消息.问题是我将类标签1和2(在MNIST中可以找到的标签)用作对象，但是在二进制分类中，您将这些值与0和1进行了比较，因此该模型无法收敛，因为sigmoid()(请参见我的代码) )只能从0到1(是一个概率).

I got the error. The problem was that I used as class labels 1 and 2 (the one you can find in MNIST), but in binary classification you compare those values with 0 and 1, so the model could not converge, since sigmoid() (see my code) can only go from 0 to 1 (is a probability).

使用0和1代替1和2可以很好地解决此问题.现在我的模型收敛到98％的精度:-)

Using 0 and 1 instead of 1 and 2 solved the problem beatifully. Now my model converges to 98% accuracy :-)

感谢大家的帮助！

关于，翁贝托

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